迈向工业视觉中的全在线无监督异常检测与定位
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision
AI总结：
本文提出全在线无监督异常检测方法LeMO，通过正交随机噪声初始化可学习记忆并结合对比学习损失进行在线联合优化，在在线、离线及少样本场景中均表现优异。
摘要：
尽管现有的图像异常检测方法取得了令人瞩目的结果，但它们大多是一种离线学习范式，需要预先收集大量数据，这限制了它们在具有在线流式数据的工业场景中的适应性。基于在线学习的图像异常检测方法与工业在线流式数据更兼容，但很少受到关注。本文首次提出了一种全在线学习的图像异常检测方法，即LeMO，为在线图像异常检测学习记忆。LeMO利用以正交随机噪声初始化的可学习记忆，消除了记忆初始化中对大量数据的需求，并规避了离线数据收集的低效性。此外，设计了一种基于对比学习的异常检测损失函数，以实现记忆和面向图像目标特征的在线联合优化。所提出的方法简单且高效。大量实验证明了LeMO在在线设置下的优越性能。此外，在离线设置下，LeMO也与当前最先进的方法具有竞争力，并在少样本场景中取得了优异的性能。
英文摘要：
Although existing image anomaly detection methods yield impressive results, they are mostly an offline learning paradigm that requires excessive data pre-collection, limiting their adaptability in industrial scenarios with online streaming data. Online learning-based image anomaly detection methods are more compatible with industrial online streaming data but are rarely noticed. For the first time, this paper presents a fully online learning image anomaly detection method, namely LeMO, learning memory for online image anomaly detection. LeMO leverages learnable memory initialized with orthogonal random noise, eliminating the need for excessive data in memory initialization and circumventing the inefficiencies of offline data collection. Moreover, a contrastive learning-based loss function for anomaly detection is designed to enable online joint optimization of memory and image target-oriented features. The presented method is simple and highly effective. Extensive experiments demonstrate the superior performance of LeMO in the online setting. Additionally, in the offline setting, LeMO is also competitive with the current state-of-the-art methods and achieves excellent performance in few-shot scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
